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arXiv 2023年

作者： Lin, Kunyang Chen, Peihao Huang, diwei Li, Thomas H. Tan, Mingkui Gan, Chuang South China University of Technology United States Information Technology R&D Innovation Center of Peking University China UMass Amherst United States MIT-IBM Watson AI Lab United States Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Peking University Shenzhen Graduate School China

Vision-and-language navigation (VLN) requires an embodied agent to navigate in realistic 3d environments using natural language instructions. Existing VLN methods suffer from training on small-scale environments or unreasonable path-instruction datasets, limiting the generalization to unseen environments. There are massive house tour videos on YouTube, providing abundant real navigation experiences and layout information. However, these videos have not been explored for VLN before. In this paper, we propose to learn an agent from these videos by creating a large-scale dataset which comprises reasonable path-instruction pairs from house tour videos and pre-training the agent on it. To achieve this, we have to tackle the challenges of automatically constructing path-instruction pairs and exploiting real layout knowledge from raw and unlabeled videos. To address these, we first leverage an entropy-based method to construct the nodes of a path trajectory. Then, we propose an action-aware generator for generating instructions from unlabeled trajectories. Last, we devise a trajectory judgment pretext task to encourage the agent to mine the layout knowledge. Experimental results show that our method achieves state-of-the-art performance on two popular benchmarks (r2r and rEVErIE). Code is available at https://***/JeremyLinky/YouTube-VLN Copyright © 2023, The Authors. All rights reserved.

关键词： Navigation

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reference-State Error Mitigation: A Strategy for High Accuracy Quantum Computation of Chemistry

arXiv

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arXiv 2022年

作者： Lolur, Phalgun Skogh, Mårten Warren, Christopher Biznárová, Janka Osman, Amr Tancredi, Giovanna Wendin, Göran Bylander, Jonas rahm, Martin Department of Chemistry and Chemical Engineering Chalmers University of Technology Gothenburg Sweden Data Science & Modelling Pharmaceutical Science R&D AstraZeneca Gothenburg Sweden Department of Microtechnology and Nanoscience MC2 Chalmers University of Technology Gothenburg Sweden

decoherence and gate errors severely limit the capabilities of state-of-the-art quantum computers. This work introduces a strategy for reference-state error mitigation (rEM) of quantum chemistry that can be straightforwardly implemented on current and near-term devices. rEM can be applied alongside existing mitigation procedures, while requiring minimal post-processing and only one or no additional measurements. The approach is agnostic to the underlying quantum mechanical ansatz and is designed for the variational quantum eigensolver (VQE). Two orders-of-magnitude improvement in the computational accuracy of ground state energies of small molecules (H2, HeH+ and LiH) is demonstrated on superconducting quantum hardware. Simulations of noisy circuits with a depth exceeding 1000 two-qubit gates are used to argue for scalability of the method. © 2022, CC BY-SA.

关键词： Ground state

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Enhancing Lifetime in Wireless Sensor Networks through Image Processing-Based Clustering with Genetic Algorithm routing

Enhancing Lifetime in Wireless Sensor Networks through Image...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： M. Ilampari M. Amina Begum N Mahesh Babu S. Gopi M. Mythreyee Shanmugavel deivasigamani Saveetha School of Law SIMATS Department of ECE Oxford College of Engineering Chennai India Department of Information Technology Anurag Engineering College Suryapet Kodad Telangana India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of EEE Dr. MGR Educational and Research Institute Chennai Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Chennai India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Wireless Sensor Networks (WSNs) are a very important technology widely used in diverse application domains of environmental monitoring, disaster management, health care, industrial automation etc. These are networks in which spatially distributed sensor nodes monitor physical or environmental conditions and communications data are processed through a base station. Here, we introduced a novel method for enhancing the efficiency and lifetime of WSNs by using the WSN to process images data and genetic programming (GA) to find the best route. A system model was developed to balance a network consisting of homogenous sensor nodes placed randomly in a particular geographical area, representing realistic scenarios. Each sensor node has limited energy resources, hence the operation of the network was partitioned into clustering phase and routing phase to optimal utilization of energy. To improve both the accuracy and efficiency of clustering, the genetic algorithm then identified the optimal path through these clusters using the best combination of pivot nodes for transmission. The proposed method applies image processing techniques to process sensor data, preprocesses sensor data, and then improves the classification process, clustering results, and the genetic algorithm obtains the more energy-efficient paths. Our extensive evaluation results prove substantial optimizations in network performance, energy efficiency and lifetime over known protocols. This method is expected to be effective in such applications as environmental monitoring, disaster management, and other applications that will utilize reliable and sustainable WSNs.

关键词： Wireless sensor networks Protocols Image processing disaster management routing Energy efficiency Environmental monitoring Security reliability Genetic algorithms

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Machine Learning for Network Traffic in Home Automation IoT Systems

Machine Learning for Network Traffic in Home Automation IoT ...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： S. P. Shingare d.Suresh Chetan K. Verma S. Lakshminarasimhan raghunandan K r r. Navaneetha Krishnan Department of Chemical Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai Department of ECE GMR Institute of Technology Andhra Pradesh India MBIT CVM University Anand Gujrat India Department Artificial Intelligence and Data Science Department of Computer Science and Engineering NMAM Institute of Technology Nitte (Deeemed to be a University) Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

IOT device security, which should protect the astute homeowner and family, has left hundreds of thousands, if not millions, of devices vulnerable to hacking. It is not practical to handle various sorts of gadgets under the same system monitoring approach since each of these devices has distinct requirements for Quality of Service (QoS). IoT Governance and Security: In combination with device Categorization, IoT security and management may monitor device activity and automate network activities based on type or function to address concerns with rogue or susceptible devices. One of the most amazing applications of machine learning-based traffic analysis is this one, which can automatically de-anonymize devices and, as a result, reveal a variety of IoT data-hiding behaviors (as shown by an expanding body of work on the subject). This paper offers a detailed investigation into each approach to evaluate its strengths and limits. Initially, we provide a high-level procedure for classifying IoT devices and explain how various attack/fix combinations may be applied at each step of the workflow. Slides displaying specifics of the main components, like: Categorization of pubdata examination of IoT traffic data gathering situations and techniques as well. The literature on the extraction of features from IoT traffic is reviewed in Chapter 5 and includes information on related classes, comparisons to common feature types, and a brief description of a free tool that can be used to extract different types of OT or IT layered data using machine learning techniques for automatic device classification. These result in the taxonomies we have created, which enumerate patterns found in various literary corpora. Additionally, the study looks at and suggests several less-traveled paths that still need more research in this emerging sector.

关键词： Machine learning Telecommunication traffic Smart homes Quality of service Feature extraction Telecommunication computing Internet of Things Security Monitoring Unsupervised learning

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diabetic retinopathy detection Using Capsule Network Modelled deep Learning

Diabetic Retinopathy Detection Using Capsule Network Modelle...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Govindharaj I Vijayarajeswari r Anbuthiruvarangan K Hannah rose Esther T Anandh r dinesh Kumar K Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology Sona College of Technology Salem Tamil Nadu India Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Manakula Vinayagar Engineering College Puducherry Tamil Nadu India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

Presently, glaucoma, an acute eye disorder, if untreated, leads to permanent blindness is a major challenging in healthcare system for the complexities of diagnosis. Historical diagnosis models greatly rely on practical assessments of retinal images in addition to, rim width, optic cup and optic disc size, and any abnormalities within the eyes. Advancements of the last few years in AI have brought recent opportunities in diagnosis of glaucoma disorder. The work focuses on a composite learning model that combines UN et++ and CapsN et design for accurate detection and diagnosis of glaucoma. Using UNet++, semantic segmentation is applied together with Od and OC segmented region being critical to the identification of illness. The hybrid model leverage of CapsNet ability to detect hierarchical relationship Successful in better detection of glaucomatous change in comparison to standard Convolutional Neural Networks. The method uses state-of-the-art approaches like CLAHE method to preprocess retinal images, as a result, to improve the image quality. When examined with algorithm models, this research survey has been trained and tested on benchmark datasets for optic curves, optic segmentation and glaucoma detection wherein it has outperformed all of them. This automated solution for glaucoma detection offers promising potential in the fight against blindness, as evidenced by the methods robust trained performance and diagnostic accuracy. The integration in UNet++ and CapsNet Offers a unique and effective solution for prior and accuracy in detection of glaucoma, potentially revolutionizing the field of ophthalmic diagnostics.

关键词： Glaucoma deep learning diabetic retinopathy Technological innovation Accuracy Biomedical optical imaging Optical computing Blindness retina Optical imaging

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Underwater Object detection using Image Enhancement and deep Learning Models

Underwater Object Detection using Image Enhancement and Deep...

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European Workshop on Visual Information Processing (EUVIP)

作者： Adane Nega Tarekegn Faouzi Alaya Cheikh Mohib Ullah Erik Tobias Sollesnes Cornelia Alexandru Saeed Nourizadeh Azar Erdeniz Erol George Suciu Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway USEA Ocean Data Oslo Norway Research & Development Department BEIA Consult International Bucharest Romania R&D and AI Department OBSS Technology Istanbul Turkey Elkon Elektrik San. Tic. A.S Istanbul Turkey

Autonomous underwater vehicles (AUVs) are efficient robotic tools, offering a wide range of applications in ocean exploration and research, such as oceanographic mapping, environmental monitoring, and archaeology. Incorporating an automatic object detection system with AUVs can substantially improve their ability to perceive and recognize objects in a complicated and often hazardous environment. Currently, detecting underwater objects relied on a man-in-the-loop approach, where AUVs captured vast amounts of data and saved them in memory for offline processing. This study investigates the use of deep learning for automatic image preprocessing and object detection, evaluating and comparing three state-of-the-art YOLO (You Only Look Once) models, including YOLOv8, YOLOv7, and YOLOv5. Extensive experiments were conducted using publicly available underwater image datasets, revealing that the pre-trained models attain superior performance on the Brackish dataset. YOLOv5 and YOLOv8 achieved the highest mean average precision (mAP) with a score of 99%, while YOLOv7 scored 89%. Furthermore, an underwater image enhancement algorithm is employed on the UrPC2021 dataset, significantly improving the detection accuracy with a 3% increase in mAP across all three models. In terms of inference speed, YOLOv5 demonstrated the highest frames per second (FPS), while maintaining comparable performance in mAP and recall.

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revolutionizing Cardiovascular Attack Prediction: A Comprehensive Machine Learning Approach for Accurate and Timely detection

Revolutionizing Cardiovascular Attack Prediction: A Comprehe...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： S. durai d. Jaganathan Prabhu V r. Harini Dept. of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Artificial Intelligence Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Thiruvallur India Artificial Intelligence & Data Science vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College Chennai India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Cardiac attacks are a leading cause of mortality worldwide, underscoring the critical need for timely and accurate healthcare detection. Predicting cardiovascular attacks is a formidable challenge in clinical data analysis. Machine Learning (ML) proves to be a valuable tool for analyzing vast healthcare data. This innovative approach focuses on employing ML techniques to identify significant features, thereby improving the accuracy of cardiovascular attack prediction. A variety of characteristics and well-known classifiers, including Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), random Forest (rF), decision Tree (dT), and Logistic regression (Lr), are used to create the final prediction model. Through collaborative efforts, these algorithms pave the way for binary classification, effectively distinguishing between true and false outcomes. By analyzing their accuracies, this approach enables the possibilities for successful predictions.

关键词： Support vector machines Logistic regression Accuracy Cardiac arrest Nearest neighbor methods Prediction algorithms Bayes methods

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Challenges and Best Practices in Corporate AI Governance: Lessons from the Biopharmaceutical Industry

SSRN

引用

SSrN 2022年

作者： Mökander, Jakob Sheth, Margi Gersbro-Sundler, Mimmi Blomgren, Peder Floridi, Luciano Oxford Internet Institute University of Oxford 1 St Giles’ OxfordOX1 3JS United Kingdom Center for Information Technology Policy Princeton University PrincetonNJ08544 United States R&D Data Office Data Sciences and AI BioPharmaceuticals R&D AstraZeneca Cambridge United Kingdom Department of Legal Studies University of Bologna Via Zamboni 33 Bologna40126 Italy

While the use of artificial intelligence (AI) systems promises to bring significant economic and social benefits, it is also coupled with ethical, legal, and technical challenges. Business leaders thus face the question of how to best reap the benefits of automation whilst managing the associated risks. As a first step, many companies have committed themselves to various sets of ethics principles aimed at guiding the design and use of AI systems. So far so good. But how can well-intentioned ethical principles be translated into effective practice? And what challenges await companies that attempt to operationalize AI governance? In this article, we address these questions by drawing on our first-hand experience of shaping and driving the roll-out of AI governance within AstraZeneca, a biopharmaceutical company. The examples we discuss highlight challenges that any organization attempting to operationalize AI governance will have to face. These include questions concerning how to define the material scope of AI governance, how to harmonize standards across decentralized organizations, and how to measure the impact of specific AI governance initiatives. By showcasing how AstraZeneca managed these operational questions, we hope to provide project managers, CIOs, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks within other organizations with generalizable best practices. In essence, companies seeking to operationalize AI governance are encouraged to build on existing policies and governance structures, use pragmatic and action-oriented terminology, focus on risk management in development and procurement, and empower employees through continuous education and change management. © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

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Hybrid Model for Lung Cancer Prognosis: Integrating SVM and 3d CNN on Nodule data

Hybrid Model for Lung Cancer Prognosis: Integrating SVM and ...

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Emerging research in Computational Science (ICErCS), International Conference on

作者： Carmel Mary Belinda M J Kannan E Alex david S Sriram Kannan ravi Kumar S. Anstey Vathani Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Data Engineering Fidelity Investments Chennai India Department of Civil Engineering Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

In this research, a huge dataset of lung cancer images is processed using advanced image processing techniques gathered from different medical establishments. Images will be edited and color profiles retouched from original look into Strawberry corrected one. Key part of our research is to preprocess the images by using Gaussian preprocessing at initial front levels followed with Gabor filtering which improves clarity and focus in x-ray image. Findings also contribute to the field of biomedical science in studies related to lung cancer diagnosis, by proving that advanced image processing techniques are useful. These techniques are essential tools in many medical fields ensuring high quality imaging, which allows early detection and treatment planning. Lung cancer, characterized by rapid uncontrolled proliferation of cells in the lungs with a potential for distant spread either via blood or lymphatic system requires precise diagnostic techniques. After preprocessing, we apply segmentation and enhancement techniques on images to detect the salient features separating normal from affected lung tissues. Extensive experiments are conducted to validate our method compared with other methods using pixel level percentage and mask labels for anomaly detection. This work highlights the importance of a robust imaging approach for health care, especially in biomedical research where various diseases could be detected and treated.

关键词： Image segmentation Three-dimensional displays Lungs Lung cancer Feature extraction Vectors Gabor filters X-ray imaging Medical diagnostic imaging Image enhancement

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dynamic Key Generationfor Secure File Sharing System Using Time Stamp

Dynamic Key Generationfor Secure File Sharing System Using T...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S H Annie Silviya C M Hilda Jerlin N Mohana Suganthi V Helen Sathiya r Jayashree N Oviya Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India Artificial Intelligence and Data Science Panimalar Engineering College Chennai India School of Computing Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Computer Science and Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

dynamic key generation for secure file sharing system using timestamp is a novel approach to military-based secure file sharing, which adds an additional layer of security to AES-2S6 which is prominently considered as the military-grade encryption. It is designed to enhance the confidentiality and integrity of transmitted files. When a Sender uploads a file, the system prompts for a character, converts it to its ASCII value, adds the current system time value to it and utilizes this aggregated value as a unique code to secure the uploaded file. To retrieve a file, the user must possess either the file key or initiate the retrieval process during the exact time of the original upload or in the next precise clock cycle. The combination of these factors provides an extra layer of security, making unauthorized access significantly more challenging.

关键词： Hands Protocols Codes receivers real-time systems database systems Peer-to-peer computing Encryption ASCII Clocks

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